---
license: mit
---

# Gemma 2b Residual Stream SAEs. 

This is a "quick and dirty" SAE release to unblock researchers. These SAEs have not been extensively studied or characterized. 
However, I will try to update the readme here when I add SAEs here to reflect what I know about them. 

These SAEs were trained with [SAE Lens](https://github.com/jbloomAus/SAELens) and the library version is stored in the cfg.json.

All training hyperparameters are specified in cfg.json.

They are loadable using SAE via a few methods. A method that currently works (but may be replaced shortly by a more convenient method) would be the following:

```python
import torch
from sae_lens.training.session_loader import LMSparseAutoencoderSessionloader

torch.set_grad_enabled(False)
path = "path/to/folder_containing_cfgjson_and_safetensors_file"
model, sae, activation_store = LMSparseAutoencoderSessionloader.load_pretrained_sae(
    path, device = "cuda",
)
```

## Resid Post 0

Stats:
- 16384 Features (expansion factor 8)
- CE Loss score of 99.1% (2.647 without SAE, 2.732 with the SAE)
- Mean L0 54 (in practice L0 is log normal distributed and is heavily right tailed).
- Dead Features: We think this SAE may have ~2.5k dead features. 

Notes:
- This SAE was trained with methods from the Anthropic [April Update](https://transformer-circuits.pub/2024/april-update/index.html#training-saes) excepting activation normalization. 
- It is likely under-trained.


## Resid Post 6

Stats:
- 16384 Features (expansion factor 8) achieving a CE Loss score of
- CE Loss score of 95.33% (2.647 without SAE, 3.103 with the SAE)
- Mean L0 53 (in practice L0 is log normal distributed and is heavily right tailed).
- Dead Features: We think this SAE may have up to 7k dead features. 

Notes:
- This SAE was trained with methods from the Anthropic [April Update](https://transformer-circuits.pub/2024/april-update/index.html#training-saes)
  - Excepting activation normalization. 
  - We increased the learning rate here by one order of magnitude in order to explore whether this resulted in faster training (in particular, a lower L0 more quickly)
    - We find in practice that the drop in L0 is accelerated but this results is significantly more dead features (likely causing worse reconstruction)
- As above, it is likely under-trained.